Active Learning

What is Active Learning?

Active learning is a semi-supervised machine learning technique where the learning algorithm actively queries the user or an oracle for labels on the most informative instances in the dataset. The main goal of active learning is to minimize the amount of labeled data required to achieve high model performance, thereby reducing the time and cost associated with manual labeling.

How does Active Learning work?

Active learning typically involves the following steps:

  1. Train an initial model on a small set of labeled data.
  2. Use the model to predict labels for the remaining unlabeled data.
  3. Identify the most informative instances based on some criterion, such as uncertainty or information gain.
  4. Request labels for the selected instances from the oracle (usually a human expert).
  5. Update the model with the newly labeled instances and repeat the process until a satisfactory performance is achieved or the budget for labeling is exhausted.

More resources on Active Learning

To learn more about active learning, you can explore the following resources: